The Internet began as a distributed database of limited textual information, and quickly transformed into an extensive database of video, images, text, audio, data and encrypted information. The varieties of information continue to expand and our current use of text based search engines severely limits the effectiveness, and efficiency of our Internet searches. It would take several lifetimes to hunt for various kinds of information throughout the Internet and USENET news groups, and, all the while, the number of files would be expanding faster than anyone's ability to pursue them.
Search engines were devised to manage the hunt. Search engines are programs that search the Internet for documents that contain specified keywords and return a list of documents which contain those keywords. These engines run programs called “web-crawlers” or “spiders” that continuously explore the Internet and, often, USENET news groups, they index the information on websites that the web-crawlers or spiders encounter. Indexing forms a vast database of website addresses that are associated with key words that have been found on the websites themselves.
Search engines such as Yahoo, Google, MSN, and International Business Machines' CLEVER require the user to enter at least one key term or query into a text field. Keywords, phrases, phrases in quotes, and Boolean queries are matched to various sites on the Internet, and when the query is complete a list of these sites is displayed for the user's review with convenient hyper-links.
Although the most widely used search engines have a category that enables them to access images, none of them allows an image to be entered as a query or search entity. All known engines require that the user enter a text query, and the search hits files that display images that are associated with the entered text query. If a person sees an image and wishes to access online information about it, he or she will have to search for it using a text query. The user cannot use the image itself as a query. If the user cannot put his or her search request into words, he or she will not be able to conduct a search in a standard online search engine.
Several innovators are working to solve this need. Hewlett-Packard, for example, has developed a method of indexing an image that is based on information derived from a global positioning system (GPS). The system obtains an image along with its location, and indexes images according to their location. Such systems are useful in organizing album data since some digital cameras can acquire GPS data and correlate it with captured imagery. However, searching is limited to images that have a significant correlation with a given location.
A search engine developed by Xerox Corporation incorporates a multi-modal browsing and clustering system to retrieve image data. The system seeks similarities between images not only in textual references, but also in other associated information such as in-links, out-links, image characteristics, text genre, and the like. However, this engine is limited to specific image types which have defined colors, contain text, and have other visual identifiers. In short, the Xerox engine requires the images to have such specific characteristics, it limits the system's utility and viability as an all purpose search engine.
Some attempts have been made to extract information from databases using images themselves as search entities rather than keywords related to the images. These systems can translate, provide information about, or interpret objects contained in an image. These systems generally work as follows. An input device extracts the object of interest from its background. The object is compared with objects stored in a pre-populated database to find a match. Finally, the system retrieves information in the database about the object and permits it to be displayed to the user. However, the system is limited to images containing extractable, defined objects, such as fruits, articles, animals, or any object which is easily outlined. However many images require identification as a whole entity, such as an image of a geographic locations or a piece of artwork. As a result, this method has limited applicability.
Complex images with a myriad of superfluous objects are easier to identify using methods such as pixel analysis. Using this method, a database is populated with primitive, weighted vectors of images that facilitate the image processing. The inputted images are compared and matched through specific vectors that define them. Therefore, there remains a clear need for a system capable of capturing images, converting those images into computer readable formats, using the processed images as search queries in a search engine, comparing the images to images stored in the database, and, upon finding a match, displaying information associated with the image to a user of the system.
So far attempts to develop and solve such a general method for accessing electronic data via an image search engine have been fraught with multiple types and levels of problems. Overcoming long standing image matching problems is very challenging in the face of different capture angles, different orientations, different perspectives, different zoom factors, lens differences, differences in lighting sources (point and ambient), difference in shadowing, differences in time of day, differences in time of year, differences in Earth surface locations of image capture different atmospheric conditions, differences in capture pixel resolution and differences in encoding schema, etc. Further, classification and encoding of the images within databases is itself very problematic with context and content definitions being extremely difficult to standardize upon.
Many companies and individuals are working diligently to solve the hard problems associated with general image searching including such giants as Google and Microsoft. Incremental progress to date points to a long difficult development cycle to solve the general image search problems.
While others continue to struggle trying to solve the general image search problem, viable niche image search solutions have been ignored. This lack of viable niche image search solutions within the prior art is clearly demonstrated by their lack of popular use within the current multibillion-dollar Internet Search Engines.
Automated Facial Image Linking to Endorsements, Marketing, Advertising, and/or Points-of-Sale for Related Products and Services
Additional lacks within the prior art are utilization and exploitation of highly developed Human capacities to recognize Human faces and related affective emotions associated with those faces. Darwinian Natural Selection of Humans who are social animals have over the history of mankind directly selected for such highly developed facial recognition skills within successful Human descendents alive today. To a limited extent the advertising community has succeeded in utilizing our celebrity driven popular culture to relate celebrity faces to positive emotions that affectively aid in selling celebrity related products and services. Typically, the advertising community manually works piecemeal with signed celebrities to produce stove-pipe ad campaigns whose effectiveness can only be grossly estimated as a ratio of increased sales to the costs of the celebrity advertising. Specifically, lacking in the prior art are:    1. An automated convenient method for linking celebrity facial images to all their related endorsements, marketing, advertising, and points-of-sale of products and services;    2. Automated convenient methods for linking any model's, actor's, or any person's facial images to their related endorsements, marketing, advertising, and points-of-sale of products and services;    3. Automated convenient methods of tracking accessing of celebrities' information by consumers or users, to accessing endorsements, marketing, advertising, and/or points-of-sale for celebrity related products and services;    4. Reverse methods of tracking products, services and even points of sale (stores, brands, on-line web sites, etc) of interest to the celebrities that endorse, market, and/or advertise them.Automated Facial Image Linking for Emotional and Health Monitoring and Diagnose.
Darwinian Natural Selection of Humans has also highly developed the capacity to judge other Human individuals health and emotions based upon the individuals face. Specifically, facial skin coloring, dark circles under the eyes, redness of eyes, lips, cheeks, ears, etc all give clues to the relative health of an individual Human. Doctors and medical staff routinely note differences in an individual's facial characteristic as a key component of monitoring and diagnosing their patients. Unfortunately, the current status quo requires the doctors and medical staff to physically walk rounds to view patients on a periodic basis with the heavy burden and workload of memory and comparison from prior visits being required within the brain of all concerned medical staff and doctors. Such manual methods are slow, lack consistency and reliability, and are error prone. Specifically lacking are automated methods to conveniently periodically image capture patients facial images so that doctors and medical staff can view the time lapse facial images to judge progress of patients, and/or aid in diagnoses. Further lacking in the prior art are the use of automated means to read and record the universal emotions from patients' faces utilizing the Facial Action Coding System. A patient's morale and emotional state are so important to their recovery, yet no automated means is utilized to judge the patients in such a manner.
Automated Real-Time Semi-Transparent Superimposed Facial Expression Mimicry
Darwinian Natural Selection of Humans has also highly developed the capacity to mimic the actions of other Human individuals. The mere act of watching another Human performing a task activates Mirror Neurons within the watching Human that correspond to the same areas of the nervous system as the human actually performing the task. These innate mimic abilities seem to work over a wide range of viewing angles and distances. Facial expression mimicry is a specially developed ability in social Humans. However, the prior art is lacking in the use of real-time semi-transparent superimposing of a person face wishing to mimic the facial expression of another Human.
Key-Face indexing and retrieval of Internet, USENET, and other resources, analogous to the current textural key-word indexing and retrieval of same is currently lacking.
As highlighted in the prior paragraphs multiple unobvious commercially viable niche facial image recognition search engine solutions to the general image search engine problem have not been discovered nor implemented in the prior art.